53 citations · 110 across the 17 of their papers we have counts for
18 papers · 1 filter
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Dmitry Molchanov, Alexander Lyzhov, Yuliya Molchanova +2
Test-time data augmentationaveraging the predictions of a machine learning model across multiple augmented samples of datais a widely used technique that improves the predict…
Deep Curvature Suite
Diego Granziol, Xingchen Wan, Timur Garipov
We present MLRG Deep Curvature suite, a PyTorch-based, open-source package for analysis and visualisation of neural network curvature and loss landscape. Despite of providing rich…
The Implicit Metropolis-Hastings Algorithm
Kirill Neklyudov, Evgenii Egorov, Dmitry Vetrov
Recent works propose using the discriminator of a GAN to filter out unrealistic samples of the generator. We generalize these ideas by introducing the implicit Metropolis-Hastings…
Importance Weighted Hierarchical Variational Inference
Artem Sobolev, Dmitry Vetrov
Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiveness is determined by the expressivity of the utilized variational distributions in…
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha +2
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint dist…
Variational Dropout via Empirical Bayes
Valery Kharitonov, Dmitry Molchanov, Dmitry Vetrov
We study the Automatic Relevance Determination procedure applied to deep neural networks. We show that ARD applied to Bayesian DNNs with Gaussian approximate posterior distribution…